parser.add_argument('--weightdecay', type=float, default=0.0) parser.add_argument('--model', type=str, default='vgg') parser.add_argument('--resume', type=str, default=None) parser.add_argument('--datadir', type=str, default='/Users/wjf/datasets/SVHN/train25k_test70k') parser.add_argument('--logdir', type=str, default='logs/GD') args = parser.parse_args() logger = LogSaver(args.logdir) logger.save(str(args), 'args') # data dataset = SVHN(args.datadir) logger.save(str(dataset), 'dataset') train_list = dataset.getTrainList(args.batchsize, True) test_list = dataset.getTestList(1000, True) # model start_iter = 0 lr = args.lr if args.model == 'resnet': from resnet import ResNet18 model = ResNet18().cuda() elif args.model == 'vgg': from vgg import vgg11 model = vgg11().cuda() else: raise NotImplementedError() criterion = torch.nn.CrossEntropyLoss().cuda() optimizer = torch.optim.SGD(model.parameters(),
parser.add_argument('--list-size', type=int, default=5000) parser.add_argument('--sigma', type=float, default=1e-3) parser.add_argument('--resume', type=str, default=None) parser.add_argument('--datadir', type=str, default='/home/wjf/datasets/SVHN/train25000_test70000') parser.add_argument('--logdir', type=str, default='logs/GLD') args = parser.parse_args() logger = LogSaver(args.logdir) logger.save(str(args), 'args') # data dataset = SVHN(args.datadir) logger.save(str(dataset), 'dataset') train_list = dataset.getTrainList(args.list_size, True) test_list = dataset.getTestList(1000, True) # model start_iter = 0 model = vgg11().cuda() logger.save(str(model), 'classifier') criterion = nn.CrossEntropyLoss().cuda() optimizer = torch.optim.SGD(model.parameters(), lr=args.lr) logger.save(str(optimizer), 'optimizer') if args.resume: checkpoint = torch.load(args.resume) start_iter = checkpoint['iter'] model.load_state_dict(checkpoint['model']) optimizer.load_state_dict(checkpoint['optimizer'])
parser.add_argument('--ckptdir', type=str, default=None) parser.add_argument('--start', type=int, default=3) parser.add_argument('--end', type=int, default=16) parser.add_argument('--datadir', type=str, default='/home/wjf/datasets/SVHN/train25000_test70000') parser.add_argument('--logdir', type=str, default='flat_logs/GD') args = parser.parse_args() logger = LogSaver(args.logdir) logger.save(str(args), 'args') # data dataset = SVHN(args.datadir) logger.save(str(dataset), 'dataset') train_list = dataset.getTrainList(5000, True) test_list = dataset.getTestList(5000, True) # model model = vgg11().cuda() logger.save(str(model), 'classifier') criterion = nn.CrossEntropyLoss().cuda() # writer writer = SummaryWriter(args.logdir) # eval flatness torch.backends.cudnn.benchmark = True for i in range(args.start, args.end): ckpt_file = os.path.join(args.ckptdir, 'iter-' + str(i * 1000) + '.pth.tar')